@article{4650,
  author = {Duan Fugui},
  title = {Enhancing College Students' Confidence with Multi-Channel Stress Management Intervention Technology: A Data-Driven Approach},
  journal = {Journal of E-Technology},
  year = {2026},
  volume = {17},
  number = {1},
  doi = {https://doi.org/10.6025/jet/2026/17/1/10-18},
  url = {https://www.dline.info/jet/fulltext/v17n1/jetv17n1_2.pdf},
  abstract = {The paper explores the application of multi channel stress management intervention technology to improve
college students' mental health within China's educ- ational context. It critiques traditional, static mental
health education models as outdated and inefficient, arguing for the integration of computer technology and
big data analytics to modernize mental health support. The proposed model leverages data mining, filtering
algorithms, and cross platform data collection to tailor interventions to individual student needs. A key
methodological component involves the "three line translation theory" to optimize parameter settings and
enhance algorithmic precision in psychological course delivery. The study conducted a trial at a major
university, comparing a control group receiving conventional instruction with an experimental group
receiving the new intervention model. Results showed significantly higher mental health scores and improved
learning efficiency in the experimental group, as detailed in performance tables. The research underscores
the potential of technology driven, adaptive mental health frameworks to align with evolving student
cognitive patterns and educational reforms. Despite promising outcomes, the paper acknowledges
limitations, particularly delays in capturing students' real time psychological states during retesting. Overall,
the study advocates for a systemic shift toward data informed, dynamic mental health education strategies
grounded in multi channel intervention technologies, aiming to enrich teaching materials and improve
student wellbeing in higher education.},
}